Representation and discovery of knowledge with approximate computation

1.

Subject title

Representation and discovery of knowledge with approximate computation

Репрезентација и откривање на знаење со приближнo пресметување

2.

Code

m23_s_039

3.

Study program

Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software for embedded systems, Software Engineering, IT management, Bioinformatics, Security, Cryptography and Coding, Data science in computer science and engineering, Еducation with ICT, Cloud Computing, Statistics and Data Analytics, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Андреја Наумоски

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

During the course, students will get acquainted with the techniques of representation and the discovery of knowledge with approximate logic. Upon completion of the course, students will gain knowledge of the process of discovering knowledge with approximate logic, will know how to design systems based on approximate logic and know how to apply the approximate logic in different applications.

11.

Subject content:

Approximate logic, introduction and concepts. Memories in approximate logic. Belonging functions. Operations on sets in approximate logic. Drawing rules. Diminishing. Rough logic, introduction and concepts. Independence. Aprocimation. Positive region. Approximate associative matrices and functions. Addiction. Definition of approximately-grub logic. Definition of upper and lower approximation. Attributes selection techniques with approximate-grub logic. Descriptive and predictive modeling with approximately-grub logic. Detecting knowledge with approximately-grub logic and application in various areas (ecoinformatics, bioinformatics, business analysis, computer system design, errors detection, intelligent system control, massive data processing, rules, etc.).

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, практични вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

30 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

10 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

реализирани активности 15 и 16

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature